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Related Concept Videos

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Related Experiment Video

Updated: Jul 13, 2025

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments
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A Micro-Topography Measurement and Compensation Method for the Key Component Surface Based on White-Light

Junying Chen1, Boxuan Wang1, Xiuyu Chen1

  • 1College of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen 361000, China.

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|October 14, 2023
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Summary

Precise surface measurements are crucial for evaluating stress concentration in machined components. This study developed new methods using white-light interferometry and AI to accurately measure grinding grooves and machining residues, improving stress analysis.

Keywords:
key componentsstress concentrationsurface micro-topographywhite-light interferometry

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Area of Science:

  • Materials Science
  • Metrology
  • Surface Engineering

Background:

  • Surface stress concentration in machined components arises from grinding grooves and tool residues.
  • Accurate surface metrology is essential for evaluating this stress concentration.
  • Existing measurement techniques like white-light interferometry (WLI) face challenges with steep surface features.

Purpose of the Study:

  • To develop and validate precise measurement techniques for evaluating surface stress concentration in machined components.
  • To address measurement distortions in white-light interferometry (WLI) caused by grinding grooves.
  • To establish a practical method for characterizing surface residues and their impact on stress.

Main Methods:

  • White-light interferometry (WLI) was employed, with thresholding and cubic spline interpolation used to compensate for measurement distortions from steep grinding grooves.
  • Atomic force microscopy (AFM) was used for validation of WLI compensation results.
  • A novel method combined microscopic 3D micro-topography point clouds and super-depth-of-field fusion images for residue analysis.
  • The U-net semantic segmentation network was utilized for residue identification in fusion images.

Main Results:

  • The WLI compensation method using cubic spline interpolation showed good agreement with AFM measurements.
  • A practical method successfully acquired 3D micro-topography point clouds and super-depth-of-field fusion images of surface residues simultaneously.
  • The U-net network achieved a 91.06% recognition accuracy for identifying machining residues.
  • Integrated data provided detailed residual feature information (height, position, size).

Conclusions:

  • The developed WLI compensation technique effectively addresses measurement distortions caused by grinding grooves.
  • The combined point cloud and fusion image approach, coupled with AI, provides accurate characterization of surface residues.
  • This research offers foundational data for understanding and mitigating surface stress concentration in machined components.